Why Natural Language Processing is Difficult

Why Natural Language Processing is Difficult – Considering how challenging human language is and how differently each individual uses it.
Why natural language processing is difficult

From Google Assistant and Siri to chatbots on e-commerce sites, our interaction with Artificial Intelligence has increased significantly over the past decade.

These ‘bots’ that we interact with, almost daily are based on machine learning algorithms that equip them with the ability to understand human language. 

Whether you’re chatting with them or speaking to them, they will be able to comprehend what you’re saying and come up with an appropriate reply within seconds. 

It is almost like interacting with a human being, thanks to Natural Language Processing

Developers are working on improving the NLP algorithm to enhance the ability of bots to understand human language including the sentiments used, emotions in statements, and much more!

Considering how challenging human language is and how differently each individual uses it to communicate, NLP has got a long way to go to become fluid, consistent, and robust. Explore Why Natural Language Processing is Difficult here.

What Makes Natural Language Processing Difficult

While the tool is a powerful one with significant limitations and benefits. 

Let’s dive into some of the challenges faced by NLP when processing human dialectal:

Development Time:

A task as complex as interpreting and responding to human language is not a simple one. 

Humans use words and phrases differently, speak with different accents, use idioms, metaphors, homophones, and several other complexities of language. 

Considering this from a developer’s perspective, the task of developing an NLP becomes even more complicated. 

From evaluating billions of data points and adequately training the Artificial Intelligence, the development process is time-consuming. 

Using pre-existing NLP technologies might reduce the product building time. However, if not, then the developer will need to build the product from scratch. 

Ambiguities:

In NLP, ambiguous phrases are those which can be interpreted in more than one way. The interpretation depends on the context the word has been used in.

Some examples of ambiguity are:

Lexical Ambiguity: words that can be used as adjectives, nouns, and verbs.

Semantic Ambiguity: this refers to sentences that have different meanings in different contexts. 

For example, ‘I saw the boy with my binoculars.’ This could mean two things: either that the boy had my binoculars on, or that I saw him using my binoculars.

Syntactic Ambiguity: the confusion is created because of the two meanings of the sentence given above. “saw” or the “boy” could be modified by the phrase ‘with my binoculars’ to establish a clearer meaning. 

Spelling Errors:

Humans can easily solve spelling errors. We have the ability to understand the link between the misspelt word and the correctly spelt equivalent. 

This means we can understand the remainder of the phrase easily by correcting the spelling errors in the sentence. 

However, a machine will find it more difficult to interpret a sentence with multiple spelling errors. A Natural Language Processing Sample algorithm will be required to identify the typical misspellings, however, in some cases, it might also be unable to do so. 

Sarcasm:

NLPs face problems with sarcasm because the words typically used to express irony or sarcasm, could be positive or negative in definition but they are used to create the opposite effect.

AI based on NLP cannot differentiate between the negative and positive meanings of words and phrases intended for sarcasm. 

Cues such as ‘yeah right, ‘whatever, etc. could be interpreted differently in several contexts. 

Colloquialisms and Slang:

Colloquialisms do not have a dictionary definition when used in formal language and the meanings of such words will vary based on the geographical location. 

Cultural slang makes it even harder for AI to process and understand what it really means in different areas. 

Informal phrases, idioms, and expressions create challenges for NLPs since they are not advanced enough to interpret what a phrase means in different territories. 

Conclusion:

The wide-ranging benefits that ML Solution brings to businesses are undeniable. 

Considering the latest versions springing up daily with new and more advanced developments, it is inevitable for this technology to experience significant improvements over the coming years. 

Muhammad Imran is a regular content contributor at Folio3.Ai, In this growing technological era, I love to be updated as a techy person. Writing on different technologies is my passion and understanding of new things that I can grow with the world.

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